Characterizing Cochlear implant artefact removal from EEG recordings using a real human model.
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ABSTRACT:
SUBMITTER: Undurraga JA
PROVIDER: S-EPMC8374497 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:EEG recordings made during concurrent fMRI are confounded by the pulse artefact (PA), which although smaller than the gradient artefact is often more problematic because of its variability over multiple cardiac cycles. A better understanding of the PA is needed in order to generate improved methods for reducing its effect in EEG-fMRI experiments. Here we performed a study aimed at identifying the relative contributions of three putative sources of the PA (cardiac-pulse-driven head rotation, the Hall effect due to pulsatile blood flow and pulse-driven expansion of the scalp) to its amplitude and variability. EEG recordings were made from 6 subjects lying in a 3T scanner. Accelerometers were fixed on the forehead and temple to monitor head motion. A bite-bar and vacuum cushion were used to restrain the head, thus greatly attenuating the contribution of cardiac-driven head rotation to the PA, while an insulating layer placed between the head and the EEG electrodes was used to eliminate the Hall voltage contribution. Using the root mean square (RMS) amplitude of the PA averaged over leads and time as a measure of the PA amplitude, we found that head restraint and insulating layer reduced the PA by 61% and 42%, respectively, when compared with the PA induced with the subject relaxed, indicating that cardiac-pulse-driven head rotation is the dominant source of the PA. With both the insulating layer and head restraint in place, the PA was reduced in RMS amplitude by 78% compared with the relaxed condition, the remaining PA contribution resulting from scalp expansion or residual head motion. The variance of the PA across cardiac cycles was more strongly reduced by the insulating layer than the head restraint, indicating that the flow-induced Hall voltage makes a larger contribution than pulse-driven head rotation to the variability of the PA.
| S-EPMC3601330 | biostudies-literature
Project description:Interest in electrocochleography (ECoG) has recently resurged as a potential tool to assess peripheral auditory function in cochlear implant (CI) users. ECoG recordings can be evoked using acoustic stimulation and recorded from an extra- or intra-cochlear electrode in CI users. Recordings reflect contributions from cochlear hair cells and the auditory nerve. We recently demonstrated the feasibility of using Custom Sound EP (clinically available software) to record ECoG responses in Nucleus Hybrid CI users with preserved acoustic hearing in the implanted ear (Abbas et al, 2017). While successful, the recording procedures were time intensive, limiting clinical applications. The current report describes how we improved data collection efficiency by writing custom software using Python programming language. The software interfaced with Nucleus Implant Communicator (NIC) routines to record responses from an intracochlear electrode. ECoG responses were recorded in eight CI users with preserved acoustic hearing using Custom Sound EP and the Python-based software. Responses were similar across both recording systems, but the recording time decreased significantly using the Python-based software. Seven additional CI users underwent repeated testing using the Python-based software and showed high test-retest reliability. The improved efficiency and high reliability increases the likelihood of translating intracochlear ECoG to clinical practice.
| S-EPMC6421572 | biostudies-literature
Project description:Word recognition is a gateway to language, linking sound to meaning. Prior work has characterized its cognitive mechanisms as a form of competition between similar-sounding words. However, it has not identified dimensions along which this competition varies across people. We sought to identify these dimensions in a population of cochlear implant users with heterogenous backgrounds and audiological profiles, and in a lifespan sample of people without hearing loss. Our study characterizes the process of lexical competition using the Visual World Paradigm. A principal component analysis reveals that people's ability to resolve lexical competition varies along three dimensions that mirror prior small-scale studies. These dimensions capture the degree to which lexical access is delayed ("Wait-and-See"), the degree to which competition fully resolves ("Sustained-Activation"), and the overall rate of activation. Each dimension is predicted by a different auditory skills and demographic factors (onset of deafness, age, cochlear implant experience). Moreover, each dimension predicts outcomes (speech perception in quiet and noise, subjective listening success) over and above auditory fidelity. Higher degrees of Wait-and-See and Sustained-Activation predict poorer outcomes. These results suggest the mechanisms of word recognition vary along a few underlying dimensions which help explain variable performance among listeners encountering auditory challenge.
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Project description:The outcome of cochlear implantation (CI) in patients with cochlear nerve deficiency (CND) is variable, resulting in a wide range of speech perception performance, from degrees of environmental sound perception to conversation without lip-reading. Twenty-five cochlear implantees with CND were enrolled retrospectively to determine the factors correlated with CI outcome in patients with CND and to develop a predictive model for CI outcome. CI outcome was evaluated using the Categories of Auditory Performance (CAP) score at 2 years after CI. Patients with negative auditory brainstem response (ABR) showed a significantly lower CAP score than those with positive ABR (2.5?±?1.7, 4.8?±?0.7; p?=?0.001). The area ratio of vestibulocochlear nerve (VCN) to facial nerve (FN) at the cerebellopontine angle on magnetic resonance images was positively correlated with CI outcome (p?<?0.001). With multiple regression analysis, a predictive equation accounting for 66% of variance of CAP score at 2 years after CI was [Formula: see text]. We found that preoperative ABR and area ratio of VCN to FN at the cerebellopontine angle could predict CI outcome in patients with CND. Preoperative counselling based on our predictive model might be helpful to determine treatment modality for auditory rehabilitation and which ear to implant.
| S-EPMC6362156 | biostudies-literature
Project description:IntroductionOnly a small percentage (6-10%) of patients who are candidates receive cochlear implants (CIs). One potential reason contributing to low usage rates may be confusion regarding which patients to refer for CI evaluation. The extent to which information provided by standard clinical audiologic assessments is sufficient for selecting appropriate CI evaluation referrals is uncertain. The objective of this study is to evaluate the capacity of standard clinical audiologic measures to differentiate CI candidates from noncandidates.MethodThe study design is a retrospective review of a prospectively maintained CI database from a university-based tertiary medical center of 518 patients undergoing CI evaluations from 2012 to 2020. Each ear of each patient was treated as an independent value. Receiver operating characteristic (ROCs) curves were constructed using aided AzBio sentence recognition scores in quiet and aided AzBio +10 dB signal-to-noise ratio scores <60% as binary classifiers for CI candidacy. For each ROC, we examined the capacity of multiple pure-tone thresholds, pure-tone average (PTA), and CNC word recognition scores (WRSs) measured under earphones to determine CI candidacy. Area under the curve ROC (AUC-ROC) values were calculated to demonstrate the capacity of each model to differentiate CI candidates from noncandidates.ResultsVariables with the greatest capacity to accurately differentiate CI candidates from noncandidates using aided AzBio in quiet scores were earphone CNC WRS, earphone pure-tone threshold at 1,000 Hz, and earphone PTA (AUC-ROC values = 0.86-0.88). Using aided AzBio +10 scores as the measure for candidacy, only CNC word recognition had a fair capacity to identify candidates (AUC-ROC value = 0.73). Based on the ROCs, a 1,000 Hz pure-tone threshold >50 dB HL, PTA >57 dB HL, and a monosyllabic WRS <60% can each serve as individual indicators for referral for CI evaluations.ConclusionThe current study provides initial indicators for referral and a first step at developing evidence-based criteria for CI evaluation referral using standard audiologic assessments.
| S-EPMC9133005 | biostudies-literature
Project description:BackgroundEEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts.MethodsOur fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR.ResultsThe best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs).DiscussionApart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.
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Project description:Patient movement during the acquisition of magnetic resonance images (MRI) can cause unwanted image artefacts. These artefacts may affect the quality of clinical diagnosis and cause errors in automated image analysis. In this work, we present a method for generating realistic motion artefacts from artefact-free magnitude MRI data to be used in deep learning frameworks, increasing training appearance variability and ultimately making machine learning algorithms such as convolutional neural networks (CNNs) more robust to the presence of motion artefacts. By modelling patient movement as a sequence of randomly-generated, 'demeaned', rigid 3D affine transforms, we resample artefact-free volumes and combine these in k-space to generate motion artefact data. We show that by augmenting the training of semantic segmentation CNNs with artefacts, we can train models that generalise better and perform more reliably in the presence of artefact data, with negligible cost to their performance on clean data. We show that the performance of models trained using artefact data on segmentation tasks on real-world test-retest image pairs is more robust. We also demonstrate that our augmentation model can be used to learn to retrospectively remove certain types of motion artefacts from real MRI scans. Finally, we show that measures of uncertainty obtained from motion augmented CNN models reflect the presence of artefacts and can thus provide relevant information to ensure the safe usage of deep learning extracted biomarkers in a clinical pipeline.
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Project description:Deep brain stimulation (DBS) can be a very efficient treatment option for movement disorders and psychiatric diseases. To better understand DBS mechanisms, brain activity can be recorded using magnetoencephalography (MEG) with the stimulator turned on. However, DBS produces large artefacts compromising MEG data quality due to both the applied current and the movement of wires connecting the stimulator with the electrode. To filter out these artefacts, several methods to suppress the DBS artefact have been proposed in the literature. A comparative study evaluating each method's effectiveness, however, is missing so far. In this study, we evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system: (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation (tSSS). In the sensor space, the largest increase in signal-to-noise (SNR) ratio was achieved by ICA-MI, while the best correspondence in terms of source activations was obtained by tSSS. LCMV beamforming alone was not sufficient to suppress the DBS-induced artefacts.
| S-EPMC7443703 | biostudies-literature
Project description:We present a protocol to study naturalistic human communication using dual-electroencephalography (EEG) and audio-visual recordings. We describe preparatory steps for data collection including setup preparation, experiment design, and piloting. We then describe the data collection process in detail which consists of participant recruitment, experiment room preparation, and data collection. We also outline the kinds of research questions that can be addressed with the current protocol, including several analysis possibilities, from conversational to advanced time-frequency analyses. For complete details on the use and execution of this protocol, please refer to Drijvers and Holler (2022).1.
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Project description:Despite recent interest in using zebrafish in human disease studies, sparked by their economics, fecundity, easy handling, and homologies to humans, the electrophysiological tools or methods for zebrafish are still inaccessible. Although zebrafish exhibit more significant larval-adult duality than any other animal, most electrophysiological studies using zebrafish are biased by using larvae these days. The results of larval studies not only differ from those conducted with adults but also are unable to delicately manage electroencephalographic montages due to their small size. Hence, we enabled non-invasive long-term multichannel electroencephalographic recording on adult zebrafish using custom-designed electrodes and perfusion system. First, we exploited demonstration of long-term recording on pentylenetetrazole-induced seizure models, and the results were quantified. Second, we studied skin-electrode impedance, which is crucial to the quality of signals. Then, seizure propagations and gender differences in adult zebrafish were exhibited for the first time. Our results provide a new pathway for future neuroscience research using zebrafish by overcoming the challenges for aquatic organisms such as precision, serviceability, and continuous water seepage.
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